Academic Articles & Research Papers

Analyzing Brain Tumor Classification Techniques: A Comprehensive Survey

Classification of brain tumour is critical in medical image analysis and diagnosis as it aids in determining the affinity of cases to select treatment paths. This feature underscores the importance of accurate classification of brain tumour...

Explainable Sentiment-Based Reputational Risk Modelling in Ugandan Megaprojects

Reputational risk reflected in digital public discourse can significantly influence stakeholder perceptions, public trust, and decision-making surrounding government megaprojects. Existing computational approaches primarily focus on sentime...

Natural Language Processing for Historical Texts

Natural Language Processing

What Is Natural Language Processing?

Deep learning and machine learning continues to proliferate throughout various industries, and has revolutionized the topic that I wish to discuss in this book: natural language processing (NLP). NLP is a subfield of computer science that i...

Natural Language Processing and Information Systems

Natural language processing and its future in medicine

If accurate clinical information were available electronically, automated applications could be developed to use this information to improve patient care and lower costs. However, to be fully retrievable, clinical information must be struct...

Introduction To Natural Language Processing

"The book provides a technical perspective on the most contemporary data-driven approaches, focusing on techniques from supervised and unsupervised machine learning. It also includes background in the salient linguistic issues, as well as c...

Natural language processing future

Natural Language Processing is a technique where machine can become more human and thereby reducing the distance between human being and the machine can be reduced. Therefore in simple sense NLP makes human to communicate with the machine e...

Brains and algorithms partially converge in natural language processing

Deep learning algorithms trained to predict masked words from large amount of text have recently been shown to generate activations similar to those of the human brain. However, what drives this similarity remains currently unknown. Here, w...

Natural Language Processing and Information Systems

Natural Language Processing and Systems Biology

Natural Language Processing

This chapter uncovers some of the basic techniques to tackle text data using PySpark. Today's textual form of data is being generated at a lightning pace with multiple social media platforms offering users the options to share their opinion...

Handbook of Computational Linguistics and Natural Language Processing

The Handbook of Computational Linguistics and Natural Language Processing provides a comprehensive overview of the concepts, methodologies, and applications being undertaken today in computational linguistics and natural language processing...

A Survey on Natural Language Processing for Fake News Detection

Fake news detection is a critical yet challenging problem in Natural Language Processing (NLP). The rapid rise of social networking platforms has not only yielded a vast increase in information accessibility but has also accelerated the spr...

Natural Language Processing

The AI approach applies to natural language processing. The AI communication approach uses intelligent systems. These intelligent systems, such as English, use natural languages. The process of Natural Language required a smart machine such...

Study on Building Business-oriented Resource On-demand Resolution Model

To address the issue of re-analyzing and repeating development of natural language processing tools and resource ana-lysis plugins when new requirements arise during project development,this paper proposes a business-oriented on-demand reso...

Attention Pooling-Based Bidirectional GRU Model for Sentimental Classification

Recurrent neural network (RNN) is one of the most popular architectures for addressing variable sequence text, and it shows outstanding results in many natural language processing (NLP) tasks and remarkable performance in capturing long-ter...

GBsim: A Robust GCN-BERT Approach for Cross-Architecture Binary Code Similarity Analysis

Recent advances in graph neural networks have transformed structural pattern learning in domains ranging from social network analysis to biomolecular modeling. Nevertheless, practical deployments in mission-critical scenarios such as binary...

Instance-Level Weighted Contrast Learning for Text Classification

With the explosion of information, the amount of text data has increased significantly, making text categorization a central area of research in natural language processing (NLP). Traditional machine learning methods are effective, but deep...